Kernels for structured data

Authors:
Thomas Gärtner;John W. Lloyd;Peter A. Flach
Affiliations:
Knowledge Discovery, Fraunhofer Institut Autonome Intelligente Systeme, Germany and Machine Learning, Department of Computer Science, University of Bristol, UK;Computer Sciences Laboratory, Research School of Information Sciences and Engineering, The Australian National University;Machine Learning, Department of Computer Science, University of Bristol, UK
Venue:
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Year:
2002

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Abstract

Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently have researchers started investigating kernels for structured data. This paper describes how kernel definitions can be simplified by identifying the structure of the data and how kernels can be defined on this structure. We propose a kernel for structured data, prove that it is positive definite, and show how it can be adapted in practical applications.